Immunohistochemistry Staining

Immunohistochemistry (IHC) staining, a crucial technique in pathology for identifying specific proteins in tissue samples, is often expensive and time-consuming. Current research focuses on developing computational methods, primarily using deep learning models like GANs and diffusion models, to virtually translate readily available Hematoxylin and Eosin (H&E) stained images into IHC images. This virtual staining aims to reduce costs and accelerate diagnosis, particularly in applications like breast cancer HER2 status assessment, by leveraging the correlation between morphological and molecular information in different staining types. The resulting improvements in efficiency and accessibility have significant implications for pathology workflows and research.